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基于强化模糊认知图实现数据与知识协作的氟化铝添加量决策方法
Weichao Yue, Weihua Gui, Xiaofang Chen, Zhaohui Zeng, Yongfang Xie
工程（英文） ›› 2019, Vol. 5 ›› Issue (6) : 1060-1076.
PDF(5754 KB)
PDF(5754 KB)
基于强化模糊认知图实现数据与知识协作的氟化铝添加量决策方法
A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps
在铝电解过程中，添加氟化铝能降低电解质的初晶温度，从而提高电流效率。氟化铝添加量的决策是一项复杂的知识型工作，需要考虑许多相关的因素，在实际生产中主要依赖于人工经验。由于工艺人员的主观性以及铝电解槽的复杂性，基于知识或者基于数据的决策方法难以保证添加的准确性。现有的决策方法难以囊括复杂的因果关系。本文针对氟化铝添加量的决策提出了一种基于强化模糊认知图的数据与知识协作策略。在这种方法中，改进的模糊k均值和模糊决策树用于提取模糊规则，其中提取的规则用于修正专家提出的初始框架。同时，采用状态转移优化算法（STA）获取强化模糊认知图的权重。将提出的方法与已有方法进行对比，结果表明，强化模糊认知的收敛速度快于基于Hebbian学习方法、粒子群优化方法以及遗传算法。不仅如此，基于所提方法氟化铝添加量的决策准确率高于其他方法。因此，针对氟化铝添加量的决策，本文提出的方法是有效的。
In the aluminum reduction process, aluminum fluoride (AlF3) is added to lower the liquidus temperature of the electrolyte and increase the electrolytic efficiency. Making the decision on the amount of AlF3 addition (referred to in this work as MDAAA) is a complex and knowledge-based task that must take into consideration a variety of interrelated functions; in practice, this decision-making step is performed manually. Due to technician subjectivity and the complexity of the aluminum reduction cell, it is difficult to guarantee the accuracy of MDAAA based on knowledge-driven or data-driven methods alone. Existing strategies for MDAAA have difficulty covering these complex causalities. In this work, a data and knowledge collaboration strategy for MDAAA based on augmented fuzzy cognitive maps (FCMs) is proposed. In the proposed strategy, the fuzzy rules are extracted by extended fuzzy k-means (EFKM) and fuzzy decision trees, which are used to amend the initial structure provided by experts. The state transition algorithm (STA) is introduced to detect weight matrices that lead the FCMs to desired steady states. This study then experimentally compares the proposed strategy with some existing research. The results of the comparison show that the speed of FCMs convergence into a stable region based on the STA using the proposed strategy is faster than when using the differential Hebbian learning (DHL), particle swarm optimization (PSO), or genetic algorithm (GA) strategies. In addition, the accuracy of MDAAA based on the proposed method is better than those based on other methods. Accordingly, this paper provides a feasible and effective strategy for MDAAA.
氟化铝添加 / 模糊认知图 / 学习方法 / 状态转移优化方法 / 模糊决策树
AlF 3 addition / Fuzzy cognitive maps / Learning algorithms / State transition algorithm / Fuzzy decision trees
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